citation-map 3.12

A simple tool to generate your Google Scholar citation world map.

Google Scholar Citation World Map

Chen Liu, CS PhD Candidate at Yale University.

Research areas: Machine Learning, Spatial-Temporal Modeling, Medical Vision, AI4Science.

News

[Jul 28, 2024] Version 3.10 released >>> Logic update. Tested on a professor's profile with 10,000 citations!

[Jul 27, 2024] Version 2.0 released >>> 10x speedup with multiprocessing (1 hour to 5 minutes for my profile).

[Jul 26, 2024] Version 1.0 released >>> First working version for my profile with 100 citations.

Purpose

This is a simple Python tool to generate an HTML citation world map from your Google Scholar ID.

It is easy to install (pip install citation-map, available on PyPI) and easy to use (see the Usage section).

You don't need to fork this repo unless you want to make custom changes.

Expected Outcome

You will be given an HTML file as the output of the script.

If you open it on a browser, you will see your own version of the following citation world map.

Besides, there will be a csv file recording citation information.

Usage

0. If you are new to Python, you probably want to start with a distribution of Python that also helps with environment management (such as anaconda). Once you have set up your environment (for example, when you reach the stage of conda activate env39 in this tutorial), you can move on to the next step.

1. Install this tool by running the following line in your conda-accessible command line.

   pip install citation-map --upgrade
   

2. Find your Google Scholar ID.

   • Open your Google Scholar profile. The URL should take the form of https://scholar.google.com/citations?user=GOOGLE_SCHOLAR_ID. In this case, your Google Scholar ID is just the string GOOGLE_SCHOLAR_ID.
   • Please kindly ignore configuration strings such as &hl=en (host language is English) or &sortby=pubdate (sort the works by date of publication).

3. In an empty Python script, run the following. More input arguments are shown in the demo script.

   from citation_map import generate_citation_map
   
   # This is my Google Scholar ID. Replace this with your ID.
   scholar_id = '3rDjnykAAAAJ'
   generate_citation_map(scholar_id)
   

   You can take a look at the input arguments (listed below) of the function generate_citation_map in case you need those functionalities.

   Parameters
   ----
   scholar_id: str
       Your Google Scholar ID.
   output_path: str
       (default is 'citation_map.html')
       The path to the output HTML file.
   csv_output_path: str
       (default is 'citation_info.csv')
       The path to the output csv file.
   num_processes: int
       (default is 16)
       Number of processes for parallel processing.
   use_proxy: bool
       (default is False)
       If true, we will use a scholarly proxy.
       It is necessary for some environments to avoid blocks, but it usually makes things slower.
   pin_colorful: bool
       (default is True)
       If true, the location pins will have a variety of colors.
       Otherwise, it will only have one color.
   print_citing_institutions: bool
       (default is True)
       If true, print the list of citing institutions (affiliations of citing authors).
   

Limitations

1. This tool is purely based on Google Scholar. As a result, you are expected to have underestimations due to reasons such as:

   • Your Google Scholar profile is not up-to-date.
   • Some papers citing you are not indexed by Google Scholar.
   • Some authors citing you do not have Google Scholar profiles.
   • Some authors citing you do not report their affiliations.

2. Webscraping is performed, and CAPTCHA or robot check can often get us, especially if we crawl frequently. This is more often seen in highly cited users. Unless you are blocked by Google Scholar, at worst you will end up with missing several citing authors, which is not likely a huge deal for highly cited users anyways.

3. geopy.geocoders is used to convert the citing authors' affiliations to geographic coordinates. To facilitate the process, I used some simple rule-based natural language processing to clean up the affiliations. As a result, you are expected to have:

   • Underestimation if the affiliations are not found by geopy.geocoders.
   • Underestimation if we experience communication error with geopy.geocoders.
   • Overestimation if non-affiliation phrases are incorrectly identified as locations by geopy.geocoders.

   Please raise an issue or submit a pull request if you have some good idea to better process the affiliation string. Note that currently I am not considering any paid service or tools that pose extra burden on the users, such as GPT API.

Debug

1. MaxTriesExceededException or ([WARNING!] Blocked by CAPTCHA or robot check for all entries).

   • From my experience, both are good indicators that your IP address is blocked by Google Scholar due to excessive crawling (using the scholarly package).
   • One hot fix I found was to hop on a University VPN and run again. I typically experience this error after running the tool twice, and I need to disconnect and reconnect my VPN to "unblock" myself.
   • In case this does not help, you can try to change IP adress and reduce the number of processes (e.g., setting num_processes=1).
   • If you get [WARNING!] Blocked by CAPTCHA or robot check no more than several times, it's not a big deal especially if you have many citing authors.

Changelog

Version 3.11

Additional output csv that records citation information.

Version 3.10

In 3.10, I slightly improved the logic for affiliation extraction.

In 3.8, I removed multiprocessing for geopy.geocoders as per their official documentation. Also I cleaned up some unnecessary scholarly calls which further helps us not get blacklisted by Google Scholar.

In 3.7, I updated the logic for webscraping and avoided using scholarly.citeby() which is the biggest trigger of blacklisting from Google Scholar.

Now we should be able to handle users with more citations than before. I tested on a profile with 1000 citations without running into any issue.

Version 3.0

I realized a problem with how I used `geopy.geocoders`. A majority of the authors' affiliations include details that are irrelevant to the affiliation itself. Therefore, they are not successfully found in the system and hence are not converted to geographic coordinates on the world map.

For example, we would want the substring "Yale University" from the string "Assistant Professor at Yale University".

I applied a simple fix with some rule-based natural language processing. This helps us identify many missing citing locations.

Please raise an issue or submit a pull request if you have some good idea to better process the affiliation string. Note that currently I am not considering any paid service or tools that pose extra burden on the users, such as GPT API.

Version 2.0

I finally managed to **drastically speed up** the process using multiprocessing, in a way that avoids being blocked by Google Scholar.

On my personal computer, processing my profile with 100 citations took 1 hour with version 1.0 while it's now taking 5 minutes with version 2.0.

With that said, please be careful and do not run this tool frequently. I can easily get on Google Scholar's blacklist after a few runs.

Version 1.0

Very basic functionality.

This script is a bit slow. On my personal computer, it takes half a minute to process each citation. If you have thousands of citations, it may or may not be a good idea to use this script.

I tried to use multiprocessing, but unfortunately the excessive visits get me blocked by Google Scholar.

Dependencies

Dependencies (scholarly, geopy, folium, tqdm, requests, bs4, pycountry, pandas) are already taken care of when you install via pip.

Acknowledgements

This script was written under the assistance of ChatGPT-4o, but of course after intense debugging.